300-640 Zertifikatsdemo & 300-640 Lernhilfe

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Cisco 300-640 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Infrastructure Components and Architecture	30%	- Evaluate power, cooling, and efficiency requirements - Evaluate network requirements for AI workloads: bandwidth, latency, congestion control, RoCEv2, QoS - Evaluate hybrid and multi-cloud AI integration - Evaluate compute requirements: CPU, GPU, NVLink, virtualization, containerization, scalability - Evaluate storage requirements: capacity, performance, data pipelines, distributed architectures
Topic 2: AI Infrastructure Operations and Troubleshooting	20%	- Implement performance benchmarking and validation - Troubleshoot network, compute, storage, and orchestration issues - Optimize performance, security, and reliability - Monitor infrastructure: Nexus Dashboard, Intersight, telemetry, alerts - Manage health, logs, and events
Topic 3: AI Infrastructure Deployment and Data Management	30%	- Integrate AI frameworks and applications with Cisco infrastructure - Implement data management and lifecycle for AI workloads - Configure high-performance networks for AI: Cisco Nexus, NDFC, lossless fabrics - Configure compute and storage using Cisco UCS: profiles, policies, Intersight - Deploy and manage AI infrastructure with orchestration tools
Topic 4: AI Infrastructure Fundamentals	20%	- Describe types of AI infrastructure: on-premises, cloud, hybrid, edge - Describe AI use cases in data center environments - Explain Cisco AI infrastructure solutions and portfolio - Describe core components of AI infrastructure: network, compute, storage, orchestration, monitoring - Describe AI and machine learning concepts

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300-640 Lernhilfe & 300-640 Fragen Antworten

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Cisco Implementing Cisco Data Center AI Infrastructure 300-640 Prüfungsfragen mit Lösungen (Q48-Q53):

48. Frage
What is a function of NVLink technology within a high-performance AI server or cluster?

A. to provide a high-speed, low-latency direct interconnect for GPU-to-GPU communication
B. to enable remote access and virtualization of GPU resources for distributed AI development teams
C. to manage power distribution and thermal regulation across all installed GPU units
D. to establish a secure, encrypted channel for data transfer between the AI server and external storage

Antwort: A

Begründung:
NVLink provides a high-bandwidth, low-latency direct interconnect between GPUs. In AI servers and clusters, it accelerates GPU-to-GPU communication for training and inference workloads by allowing GPUs to exchange data much faster than through standard PCIe paths.

49. Frage
A server administrator must monitor the performance and utilization of GPUs in Cisco UCS servers running AI workloads. Which two metrics must be used in Cisco Intersight to accomplish these goals? (Choose two.)

A. GPU Performance
B. Monitor
C. System CPU Utilization
D. GPU Utilization
E. Explorer

Antwort: A,D

Begründung:
Cisco Intersight provides GPU-focused telemetry for UCS servers, including utilization and performance metrics. These measurements allow the administrator to monitor how heavily the GPUs are being used and whether the AI workload is receiving the expected GPU performance.

50. Frage
A company deploys a Cisco UCS environment to host AI inferencing workloads that demand low latency, high throughput, and efficient resource utilization. Which set of actions must be taken to deploy a high-performance fabric within the Cisco UCS infrastructure?

A. Connect UCS servers to a Cisco Nexus 3000 Series Switch using 100 Gb Ethernet.
Configure a separate management network using a dedicated Cisco Catalyst switch.
B. Deploy a Cisco MDS 9000 Series Switch for Fibre Channel connectivity to storage.
Use a Cisco Fabric Interconnect for management traffic only.
C. Use a single Cisco Fabric Interconnect in a standalone configuration.
Connect all UCS servers directly to it using 10 Gb Ethernet.
D. Implement a dual-fabric design with two Cisco Fabric Interconnects, and use 100 Gb Ethernet for server-to-fabric connectivity.
Configuring port channels for uplink connections.

Antwort: D

Begründung:
A dual-fabric design with redundant Cisco Fabric Interconnects provides high availability, bandwidth scale, and resilient server-to-fabric connectivity. Using 100 Gb Ethernet and port- channel uplinks supports the low-latency, high-throughput fabric required for AI inferencing workloads while improving utilization and avoiding single points of failure.

51. Frage
An engineer must monitor congestion, flow latency, and traffic drops using Cisco Nexus Dashboard and chooses to use the Traffic Analytics feature. Which Nexus Dashboard feature is automatically disabled when Traffic Analytics is enabled?

A. Flow Telemetry
B. Connectivity Analysis
C. SNMP Traps
D. Delta Analysis

Antwort: A

Begründung:
Traffic Analytics and Flow Telemetry use overlapping telemetry resources in Cisco Nexus Dashboard. When Traffic Analytics is enabled to monitor congestion, latency, and drops, Flow Telemetry is automatically disabled to avoid resource and data collection conflicts.

52. Frage
A network architect designs a Cisco AI fabric with spine-leaf topology. What is the primary advantage of this architecture for AI training workloads?

A. Reduced need for IP addressing
B. Elimination of routing protocols
C. Predictable low-latency east-west traffic
D. Simplified wireless integration

Antwort: C

Begründung:
AI workloads generate massive east-west traffic between compute nodes. Spine-leaf designs provide predictable latency and scalable bandwidth because each leaf switch maintains equal- cost paths to all spines. Routing protocols are still required, IP addressing remains necessary, and wireless integration is unrelated to AI fabric performance requirements.

53. Frage
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